Various solutions for image processing, such as, for example, various forms of filtering or enhancement of details, are well-known techniques for improving the visualization of a recorded image. Various types of compression of image information are also known, partly in order to reduce the information content of the image and thus obtain images with smaller information quantity, but also in order to adapt the image for the viewer of the image. A human has a limited capacity as a viewer to differentiate between both details and different colours and grey scales.
Systems for recording and displaying images taken in conditions where no daylight is present have used various forms of image processing to improve the information content of the recorded image. It is common practice that the contours of the objects which are present in the recorded image are enhanced. Image processing is preferably realized by mathematical methods on a digital representation of the information content of the recorded image. The Sobel operator is an example of a well-known mathematical method for enhancing the contours, a so-called edge-enhancing or edge-preserving method, on a recorded image. A common problem in the enhancement of image details is that the noise in the image is also intensified.
One example of image recording when the light conditions are such that it is difficult to use normal optical equipment is the use of IR video or IR photography, in which IR stands for infrared. Details and structure in IR video are normally constituted by small variations in signal strength within a local region. At the same time, the total dynamic range in a single image can be large. The difference in signal level between a cold region and a warm region can result in about 65,000 grey levels being able to be recorded. Typically this signal will be compressed so that its total dynamic range becomes 8 bits or 256 distinct grey levels from black to white in order to fit the video format and be better suited for presentation to an operator. The reason for this is an adaptation to video standards. A purely linear compression of the signal is almost always unsuitable, since a small region with widely differing signal level is at risk of using all the dynamic range, whereupon an image having, in principle, just a few colour and grey scale levels is obtained.
A common way of getting round this is to utilize the histogram of the image (distribution of signal levels) and, on the basis of this, determine suitable conversion, from 16 to 8 bits, for example, so that the available dynamic is not spent or used at levels at which there is no signal. Even though histogram equalization is very effective in many contexts, it is generally difficult to foresee whether the correct details will actually be accentuated. For this, other methods which give more robust results are used. One such method is to use an edge-preserving low-pass filter to produce a background image without details or structure and subtract this image from the original image in order thereby to produce the small signal variations in which the small signal variations are constituted by the details.
Edge-preserving low-pass filters are previously known and an example of such a filter is described in C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images, Proc. 1998 IEEE 6th. Int. Conf. on Computer Vision, Bombay, India. By replacing the value of each image point with the mean value of the values of neighbouring image points, a smooth image is obtained. If non-edge-preserving filters are used, image points having neighbours with widely differing signal intensity will be affected, so that they end up at a higher or lower level than they actually should.
A problem with the currently known methods for detail enhancement and noise reduction of image information is that, in the case of high edge enhancement, then a high noise level is also obtained.